Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

dc.creatorLabra, Filidor V.
dc.creatorGaray, Aldo M.
dc.creatorLachos, Victor H.
dc.creatorOrtega, Edwin M. M.
dc.date2012
dc.date2013-09-19T18:06:42Z
dc.date2016-06-30T18:26:39Z
dc.date2013-09-19T18:06:42Z
dc.date2016-06-30T18:26:39Z
dc.date.accessioned2018-03-29T01:52:58Z
dc.date.available2018-03-29T01:52:58Z
dc.identifierJournal of Statistical Planning and Inference. Elsevier, v.142, n.7, p.2149-2165, 2012
dc.identifier0378-3758
dc.identifierWOS:000304074500045
dc.identifier10.1016/j.jspi.2012.02.018
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/2379
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/2379
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1308287
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionAn extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.
dc.description142
dc.description7
dc.description2149
dc.description2165
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.languageeng
dc.publisherElsevier
dc.publisherAmsterdam
dc.relationJournal of Statistical Planning and Inference
dc.rightsfechado
dc.sourceWOS
dc.subjectCase-deletion model
dc.subjectEM algorithm
dc.subjectHomogeneity
dc.subjectLocal influence
dc.subjectNonlinear regression models
dc.subjectScale mixtures of skew-normal distributions
dc.subjectLOCAL INFLUENCE
dc.subjectMAXIMUM-LIKELIHOOD
dc.subjectINCOMPLETE-DATA
dc.subjectLINEAR-MODELS
dc.titleEstimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions
dc.typeArtículos de revistas


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